Default Prediction in the Finance Industry Based on Ensemble Learning: Combining Machine Learning and Deep Learning

Hoanh-Su Le (), Phong Le Quang Chan (), Vinh Truong Cong (), Nhat Ho Mai Minh () and Lee Jong-Hwa ()
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Hoanh-Su Le: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Phong Le Quang Chan: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Vinh Truong Cong: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Nhat Ho Mai Minh: University of Economics and Law, Ho Chi Minh City, Vietnam, Vietnam National University, Ho Chi Minh City, Vietnam
Lee Jong-Hwa: Dong-Eui University, Busan City, South Korea

Business Systems Research, 2025, vol. 16, issue 1, 198-218

Abstract: Background Financial institutions face significant challenges in predicting loan defaults, which directly impact the non-performing loan (NPL) rate. Incorrect predictions can lead to misinformed decisions and substantial financial losses. Objectives This study aims to enhance default prediction by employing advanced ensemble learning techniques in machine learning and deep learning. Methods/Approach Instead of relying on transformation, fine-tuning, or single algorithm models, this research focuses on combining multiple models using voting and stacking techniques, particularly highlighting a stacking model combining Light Gradient Boosting Machine (LGBM) and Artificial Neural Networks (ANN). Results The ensemble learning methods, especially the LGBM-LSTM and XGB-LSTM stacking models, showed higher precision in identifying borrowers who defaulted, while the LGBM-LSTM and XGB-LSTM voting models excelled in recall and achieved an F1-score 0.1% higher. Both the stacking and voting models attained AUC values close to 90%, indicating strong overall classification performance. Conclusions The findings not only contribute to the fields of lending and peer-to-peer financial operations but also offer crucial insights that aid financial organizations in making well-informed decisions regarding loan processing and management.

Keywords: default prediction; risk assessment; machine learning; deep learning; ensemble learning; online lending (search for similar items in EconPapers)
JEL-codes: G17 G21 G30 O32 (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:bit:bsrysr:v:16:y:2025:i:1:p:198-218:n:1010

DOI: 10.2478/bsrj-2025-0010

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